presentationDCHS2015
George Oberle
October 7, 2015
Institutionalizing Knowledge in Early Washington D.C.
Proposal Abstract In Democracy in America, Alexander de Tocqueville observed that “In democratic countries the science of association is the mother of science; the progress of all the rest depends upon the progress it has made.” Tocqueville noted that Americans formed numerous associations for distinct purposes and whose goals included establishing schools and churches, and spreading knowledge through the publication of information of interest to like-minded citizens. The residents of the District of Columbia, America’s federal city, sought to create a thriving metropolis crowded with a vibrant and extensive community of scholars. Many hoped a national institution would materialize to promote the expansion of knowledge and learning. This paper explores the purpose and composition of the myriad societies in the early federal city, especially scientific, literary and proto-professional societies, 1815-1850. This paper examines the shared relationships between these groups using computational tools and methods to develop network analysis using society membership lists.
#Load tools
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Data
Source American Philosophical Society: http://www.amphilsoc.org/memhist/search
American Academy of Arts and Sciences: Book of Members. http://www.amphilsoc.org/memhist/search
Academy of Natural Sciences: “List of Members and Correspondents of the Academy of Natural Sciences,” Proceedings of the Academy of Natural Sciences of Philadelphia 8 (January 1, 1856): 17–38.
More Sources
threelarge = read.csv("data/test_APS_AAAS_ANS-1849.csv")Graphing the membership APS/AAAS/ANS
#Load the data of the three large national institutions
threelarge = read.csv("data/test_APS_AAAS_ANS-1849.csv")
threelarge %>%
group_by(Year.Elected, Organization) %>%
summarize(n = n()) %>%
ggplot(aes(x=Year.Elected, y= n, color = Organization )) +
geom_line() +
ggtitle("Annual Elected Members of APS, AAAS, and ANS") + ylab("Total Members Elected") + xlab("Year Elected")
#Geomsmooth better for observing trends
threelarge %>%
group_by(Year.Elected, Organization) %>%
summarize(n = n()) %>%
ggplot(aes(x=Year.Elected, y= n, color = Organization )) +
geom_smooth() +
ggtitle("Annual Elected Members of APS, AAAS, and ANS") + ylab("Total Members Elected") + xlab("Year Elected")## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
#maybe group by decades? not sure how to do this.
threelarge %>%
filter(Year.Elected ==1840) %>%
graph.data.frame(directed = FALSE) %>%
plot(vertex.label = NA)
title("APS, AAAS, ANS Elected Members 1840")
Graph data frame of the “national” learned societies.
fourlarge = read.csv("data/four_orgs.csv")
fourlarge_df <- graph.data.frame(fourlarge, directed = FALSE)
plot (fourlarge_df)
title("Network Graph of the 'national' Learned Societies", sub = "A network graph of members with name labels",cex.main = 2, font.main= 4, col.main= "blue", cex.sub = 0.75, font.sub = 3, col.sub = "red")
Networks
V(fourlarge_df)## + 2157/2157 vertices, named:
## [1] Samuel Harrison Smith
## [2] Andrew Hunter
## [3] John Law
## [4] Alexander McWilliams
## [5] Edward Cutbush
## [6] Thomas Law
## [7] John Quincy Adams
## [8] Andrew Jackson
## [9] John C. Calhoun
## [10] Henry Clay
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## [2157] TRUEV(fourlarge_df)$type <- bipartite.mapping(fourlarge_df)$type
is.bipartite(fourlarge_df)## [1] TRUEplot(fourlarge_df, layout = layout.bipartite, vertex.label = NA)
title("APS, AAAS, ANS and Columbian Institute", sub = "A bipartite layout of members and organizations",cex.main = 2, font.main= 4, col.main= "black", cex.sub = 0.75, font.sub = 3, col.sub = "black")
full_members
we can run our bipartite projection function and use the data_members
full_members_four <- bipartite.projection(fourlarge_df)plot(full_members_four$proj2)
title("National Learned Societies", sub = "Just the Organizations",cex.main = 2, font.main= 4, col.main= "black", cex.sub = 2, font.sub = 3, col.sub = "black")
assign edges
E(full_members_four$proj2)## + 6/6 edges (vertex names):
## [1] Columbian Institute for the Promotion of Arts and Sciences--American Academy of Arts and Sciences
## [2] Columbian Institute for the Promotion of Arts and Sciences--American Philosophical Society
## [3] Columbian Institute for the Promotion of Arts and Sciences--Academy of Natural Sciences
## [4] American Academy of Arts and Sciences --American Philosophical Society
## [5] American Academy of Arts and Sciences --Academy of Natural Sciences
## [6] Academy of Natural Sciences --American Philosophical SocietyE(full_members_four$proj2)$weight## [1] 12 19 7 96 34 91Plot but hard to see.
plot(full_members_four\(proj2, edge.width = E(full_members_four\)proj2)$weight) title(“Big Three National Learned Societies”, sub = “Weighted by Shared Members”,cex.main = 2, font.main= 4, col.main= “black”, cex.sub = 2, font.sub = 3, col.sub = “black”)
This is the good one to use in paper
plot(full_members_four$proj2, edge.width = E(full_members_four$proj2)$weight*0.2, layout = layout.fruchterman.reingold(full_members_four$proj2, weights = E(full_members_four$proj2)$weight),edge.label.cex=1,vertex.shape="circle",edge.color="red", vertex.size = 30, vertex.color="light blue", asp=0, margin=0, vertex.label.dist = 0, vertex.label.cex = 1.5, vertex.label.color = "black")
title("Learned Societies", sub = "Weighted by Shared Members",cex.main = 2, font.main= 2, col.main= "black", cex.sub = 2, font.sub = 3, col.sub = "black") 
#Load the data of the all the societies so far
netsoc = read.csv("data/networksocieties.csv")Graph data frame of the “national” learned societies.
netsoc_df <- graph.data.frame(netsoc, directed = FALSE)
plot (netsoc_df)
title("Network Graph of the 'national' Learned Societies", sub = "A network graph of members with name labels",cex.main = 2, font.main= 4, col.main= "blue", cex.sub = 0.75, font.sub = 3, col.sub = "red")
Networks
V(netsoc_df)## + 6430/6430 vertices, named:
## [1] Samuel Harrison Smith
## [2] Andrew Hunter
## [3] John Law
## [4] Alexander McWilliams
## [5] Edward Cutbush
## [6] Thomas Law
## [7] John Quincy Adams
## [8] Andrew Jackson
## [9] John C. Calhoun
## [10] Henry Clay
## + ... omitted several verticesbipartite.mapping(netsoc_df)## $res
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## [6414] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [6425] TRUE TRUE TRUE TRUE TRUE TRUEV(netsoc_df)$type <- bipartite.mapping(netsoc_df)$type
is.bipartite(netsoc_df)## [1] TRUEplot(netsoc_df, layout = layout.bipartite, vertex.label = NA)
title("Learned Societies in Early Republic", sub = "A bipartite layout of members and organizations",cex.main = 2, font.main= 4, col.main= "blue", cex.sub = 0.75, font.sub = 3, col.sub = "red")
full_members
we can run our bipartite projection function and use the data_members
members_col <- bipartite.projection(netsoc_df)assign edges
E(members_col$proj2)## + 75/75 edges (vertex names):
## [1] Columbian Institute for the Promotion of Arts and Sciences--National Institute for the Promotion of Science
## [2] Columbian Institute for the Promotion of Arts and Sciences--Medical Society of the District of Columbia
## [3] Columbian Institute for the Promotion of Arts and Sciences--American Academy of Arts and Sciences
## [4] Columbian Institute for the Promotion of Arts and Sciences--Federal Government
## [5] Columbian Institute for the Promotion of Arts and Sciences--1816 Army
## [6] Columbian Institute for the Promotion of Arts and Sciences--American Philosophical Society
## + ... omitted several edgesE(members_col$proj2)$weight## [1] 14 6 12 16 10 19 7 8 7 7 7 1 7 2 107 17 5
## [18] 9 96 4 34 4 91 7 3 7 2 62 4 1 2 12 55 30
## [35] 6 1 7 10 3 8 3 2 1 1 1 1 3 1 2 3 1
## [52] 11 5 8 9 1 30 7 7 9 4 8 2 8 10 5 2 1
## [69] 2 2 144 3 53 3 5Plot but hard to see.Don’t use.
plot(members_col\(proj2, edge.width = E(members_col\)proj2)$weight) title(“Big Three National Learned Societies”, sub = “Weighted by Shared Members”,cex.main = 2, font.main= 4, col.main= “blue”, cex.sub = 2, font.sub = 3, col.sub = “red”)
plot(members_col$proj2, edge.width = E(members_col$proj2)$weight*0.2, layout = layout.fruchterman.reingold(members_col$proj2,weights = E(members_col$proj2)$weight),edge.label.cex= 3,vertex.shape="circle",edge.color="red", vertex.size = 15, vertex.color="lightblue", asp=0, margin=0, vertex.label.dist =0, vertex.label.cex = 3.5, vertex.label.color = "black")
title("Fraternal Societies", sub = "Weighted by Shared Members",cex.main = 3, font.main= 2, col.main= "black", cex.sub = 3, font.sub = 3, col.sub = "black") 
assign edges
E(members_nat\(proj2) E(members_nat\)proj2)$weight
plot(members_nat\(proj2, edge.width = E(members_nat\)proj2)\(weight*0.20, layout = layout.fruchterman.reingold(members_nat\)proj2, weights = E(members_nat\(proj2)\)weight),edge.label.cex=1,vertex.shape=“circle”,edge.color=“red”, vertex.size = 25, vertex.color=“lightblue”, asp=0, margin=0, vertex.label.dist = 0, vertex.label.cex = 1.5, vertex.label.color = “black”) title(“Large National Institutions”, sub = “Weighted by Shared Members”,cex.main = 2.5, font.main= 2, col.main= “black”, cex.sub = 2, font.sub = 3, col.sub = “black”)
Load the data of the all the societies so far
netsoc = read.csv("data/networksocieties.csv")Graph data frame of the “national” learned societies.
netsoc_df <- graph.data.frame(netsoc, directed = FALSE)
V(netsoc_df)$shape <- ifelse(netsoc_df$Location == "Washington",
"square", "circle")
plot (netsoc_df)
title("Network Graph of the 'national' Learned Societies", sub = "A network graph of members with name labels",cex.main = 2, font.main= 4, col.main= "blue", cex.sub = 0.75, font.sub = 3, col.sub = "red")
Networks
V(netsoc_df) bipartite.mapping(netsoc_df) V(netsoc_df)\(type <- bipartite.mapping(netsoc_df)\)type is.bipartite(netsoc_df) plot(netsoc_df, layout = layout.bipartite, vertex.label = NA) title(“Learned Societies in Early Republic”, sub = “A bipartite layout of members and organizations”,cex.main = 2, font.main= 4, col.main= “blue”, cex.sub = 0.75, font.sub = 3, col.sub = “red”) full_members
we can run our bipartite projection function and use the data_members
members_col <- bipartite.projection(netsoc_df)
plot(members_col$proj2)
title(“National Learned Societies”, sub = “Just the Organizations”,cex.main = 2, font.main= 4, col.main= “blue”, cex.sub = 2, font.sub = 3, col.sub = “red”)
assign edges
E(members_col\(proj2) E(members_col\)proj2)$weight
Plot but hard to see.Don’t use.
plot(members_col\(proj2, edge.width = E(members_col\)proj2)$weight) title(“Big Three National Learned Societies”, sub = “Weighted by Shared Members”,cex.main = 2, font.main= 4, col.main= “blue”, cex.sub = 2, font.sub = 3, col.sub = “red”)
This is the good one to use in paper. I made the labels black to help with display. Resize in export to 2500 width for tiff
plot(members_col$proj2, edge.width = E(members_col$proj2)$weight*0.4, layout = layout.fruchterman.reingold(members_col$proj2,
weights = E(members_col$proj2)$weight),edge.label.cex=2,vertex.shape="circle",edge.color="red", vertex.size = 10, vertex.color="lightblue", asp=0, margin=0, vertex.label.dist = 0, vertex.label.cex = 2.5, vertex.label.color = "black")
title("Fraternal Societies", sub = "Weighted by Shared Members",cex.main = 3, font.main= 2, col.main= "black", cex.sub = 2.5, font.sub = 3, col.sub = "black") 
Full Network.
societies_edges <- read_csv("Data/societies-edge-attr.csv")
societies_nodes <- read_csv("Data/societies-node-attr.csv")societies_g <- graph_from_data_frame(societies_edges, directed = FALSE, societies_nodes)uncomment to use comment to set
#png(filename = "societies-network.png", width = 2400, height = 1600, res = 300, pointsize = 10)
set.seed(845)
par(mar = c(1, 1, 1, 1))
l <- layout.fruchterman.reingold(societies_g, weights = E(societies_g))
plot(societies_g,
layout = l,
edge.width = E(societies_g)$weight * 0.4,
vertex.size = 15,
vertex.label.color = "black",
vertex.label.cex = 3,
edge.color="light blue")
title("Early American Societies", font.main= 2) 
uncomment to use comment to set
#dev.off()